class_docstr: Lexicon acquisition. produce opinion lexicon and an aspect lexicon based
  on input dataset.
  
  Attributes:
      opinion_candidate_list_curr_iter (dict): candidate opinion terms in the current iteration
      opinion_candidate_list_prev_iter (dict): opinion candidates list of previous iteration
      opinion_candidate_list (dict): opinion terms learned across all iterations
      opinion_candidates_list_final (list): final opinion candidates list
      opinion_candidate_list_raw (dict): all instances of candidate opinion terms
                                         across all iterations
      aspect_candidate_list_curr_iter (dict): candidate terms in the current iteration
      aspects_candidate_list_prev_iter(list): Aspect candidates list of previous iteration
      aspect_candidate_list (list):  aspect terms learned across all iterations
      aspect_candidates_list_final (list): final aspect candidates list
      aspect_candidate_list_raw (dict): all instances of candidate aspect terms
                                        across all iterations
docstr: Insert new terms to tables
  clear candidates lists from previous iteration
docstr_tok: ["Insert","new","terms","to","tables","clear","candidates","lists","from","previous","iteration"]
code: def _insert_new_terms_to_tables(self):
      """
      Insert new terms to tables
      clear candidates lists from previous iteration
  
      """
      self.opinion_candidate_list_prev_iter = {}
      self.opinion_candidate_list_raw = _merge_tables(
          self.opinion_candidate_list_raw,
          self.opinion_candidate_list_curr_iter,
      )
      for (
          cand_term_list
      ) in self.opinion_candidate_list_curr_iter.values():
          if len(cand_term_list) >= self.min_freq_opinion_candidate:
              new_opinion_term = _set_opinion_term_polarity(
                  cand_term_list
              )
              self.opinion_candidate_list_prev_iter[
                  str(new_opinion_term)
              ] = new_opinion_term
      self.opinion_candidate_list_curr_iter = {}
      self.opinion_candidate_list = {
          **self.opinion_candidate_list,
          **self.opinion_candidate_list_prev_iter,
      }
      self.aspects_candidate_list_prev_iter = list()
      self.aspect_candidate_list_raw = _merge_tables(
          self.aspect_candidate_list_raw,
          self.aspect_candidate_list_curr_iter,
      )
      for (
          extracted_aspect_list
      ) in self.aspect_candidate_list_curr_iter.values():
          if (
              len(extracted_aspect_list)
              >= self.min_freq_aspect_candidate
          ):
              first = extracted_aspect_list[0]
              new_aspect_entry = AspectTerm(
                  first.term, first.pos, first.lemma
              )
              if (
                  new_aspect_entry
                  not in self.aspects_candidate_list_prev_iter
              ):
                  self.aspects_candidate_list_prev_iter.append(
                      new_aspect_entry
                  )
      self.aspect_candidate_list_curr_iter = {}
      self.aspect_candidate_list = (
          self.aspect_candidate_list
          + self.aspects_candidate_list_prev_iter
      )
  
imports: import copy
  import re
  import sys
  from os import PathLike
  from tqdm import tqdm
  from nlp_architect.models.absa import TRAIN_LEXICONS, LEXICONS_OUT
  from nlp_architect.models.absa import GENERIC_OP_LEX
  from nlp_architect.models.absa.inference.data_types import Polarity
  from nlp_architect.models.absa.train.data_types import AspectTerm, DepRelation, DepRelationTerm, LoadOpinionStopLists, LoadAspectStopLists, OpinionTerm, QualifiedTerm
  from nlp_architect.models.absa.train.rules import rule_1, rule_2, rule_3, rule_4, rule_5, rule_6
  from nlp_architect.models.absa.utils import _load_parsed_docs_from_dir, _write_final_opinion_lex, _load_lex_as_list_from_csv, read_generic_lex_from_file
label_desc: Use image node_id 7 for calling the AcquireTerms obj's underlying member method code with example usage: obj._insert_new_terms_to_tables() without return types